Add in data and print summary

This data set was generated by Reddit user gigantoir https://www.reddit.com/r/NFLstatheads/comments/q73yd0/nfl_scores_20172020/ I added the 2021 data that was scrapped from https://www.footballdb.com/games/index.html and 2002-2016 data from Reddit user yuxbni76 https://www.reddit.com/user/yuxbni76

Scores <- read.csv("NFL_SCORES_2002-2021.txt", header=TRUE, sep= "\t")
Scores$Score_differential <- abs(Scores$Score_away - Scores$Score_home)
Scores$Week <- as.factor(Scores$Week)
summary(Scores)
##       Year           Week          Home               Away          
##  Min.   :2002   2      : 323   Length:5044        Length:5044       
##  1st Qu.:2006   1      : 313   Class :character   Class :character  
##  Median :2011   12     : 313   Mode  :character   Mode  :character  
##  Mean   :2011   3      : 312                                        
##  3rd Qu.:2016   14     : 304                                        
##  Max.   :2021   15     : 304                                        
##                 (Other):3175                                        
##    Score_home     Score_away    Score_differential
##  Min.   : 0.0   Min.   : 0.00   Min.   : 0.0      
##  1st Qu.:16.0   1st Qu.:14.00   1st Qu.: 4.0      
##  Median :23.0   Median :21.00   Median : 8.0      
##  Mean   :23.3   Mean   :21.14   Mean   :11.7      
##  3rd Qu.:30.0   3rd Qu.:28.00   3rd Qu.:17.0      
##  Max.   :62.0   Max.   :59.00   Max.   :59.0      
##

Team colors

Team colors were extracted from https://teamcolorcodes.com, I took the first primary color for each team and created a list that will be for later use. For the Browns and Titans I took the secondary color as it seemed more appropriate.

Team_colors <- c("SF"="#AA0000",
                 "CHI"="#0B162A",
                 "CIN"="#FB4F14",
                 "BUF"="#00338D",
                 "DEN"="#FB4F14",
                 "CLE"="#FF3C00",
                 "TB"="#D50A0A",
                 "ARI"="#97233F",
                 "LAC"="#0080C6",
                 "KC"="#E31837",
                 "IND"="#002C5F",
                 "DAL"="#041E42",
                 "MIA"="#008E97",
                 "PHI"="#004C54",
                 "ATL"="#A71930",
                 "NYG"="#0B2265",
                 "JAX"="#006778",
                 "NYJ"="#125740",
                 "DET"="#0076B6",
                 "GB"="#203731",
                 "CAR"="#0085CA",
                 "NE"="#002244",
                 "LV"="#000000",
                 "LA"="#003594",
                 "BAL"="#241773",
                 "WAS"="#773141",
                 "NO"="#D3BC8D",
                 "SEA"="#002244",
                 "PIT"="#FFB612",
                 "HOU"="#03202F",
                 "TEN"="#4B92DB",
                 "MIN"="#4F2683")

All teams

ggplot(Scores, aes(x=Week, y=Score_differential)) +
  geom_boxplot() +
  scale_x_discrete(breaks = seq(1,17,1), guide = guide_axis(n.dodge=2)) +
  facet_wrap(~Year) +
  ylab("Absolute Score Differential")

ggplot(Scores, aes(x=Week, y=Score_differential)) +
  geom_smooth(aes(as.numeric(Week), Score_differential), method = "loess") +
  scale_x_continuous(breaks = c(1,9,17)) +
  scale_y_continuous(breaks = seq(7,21,7)) +
  facet_wrap(~Year) +
  ylab("Absolute Score Differential")
## `geom_smooth()` using formula 'y ~ x'

Score_varience <- c(var(Scores[Scores$Year == 2002, ]$Score_differential),
                    var(Scores[Scores$Year == 2003, ]$Score_differential),
                    var(Scores[Scores$Year == 2004, ]$Score_differential),
                    var(Scores[Scores$Year == 2005, ]$Score_differential),
                    var(Scores[Scores$Year == 2006, ]$Score_differential),
                    var(Scores[Scores$Year == 2007, ]$Score_differential),
                    var(Scores[Scores$Year == 2008, ]$Score_differential),
                    var(Scores[Scores$Year == 2009, ]$Score_differential),
                    var(Scores[Scores$Year == 2010, ]$Score_differential),
                    var(Scores[Scores$Year == 2011, ]$Score_differential),
                    var(Scores[Scores$Year == 2012, ]$Score_differential),
                    var(Scores[Scores$Year == 2013, ]$Score_differential),
                    var(Scores[Scores$Year == 2014, ]$Score_differential),
                    var(Scores[Scores$Year == 2015, ]$Score_differential),
                    var(Scores[Scores$Year == 2016, ]$Score_differential),
                    var(Scores[Scores$Year == 2017, ]$Score_differential),
                    var(Scores[Scores$Year == 2018, ]$Score_differential),
                    var(Scores[Scores$Year == 2019, ]$Score_differential),
                    var(Scores[Scores$Year == 2020, ]$Score_differential),
                    var(Scores[Scores$Year == 2021, ]$Score_differential))
Score_varience <- as.data.frame(Score_varience)

Score_varience$Year <- c(2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,
                         2012,2013,2014,2015,2016,2017,2018,2019,2020,2021)
Score_varience$Year <- as.factor(Score_varience$Year)


ggplot(Score_varience, aes(x=Year, y=Score_varience)) +
  geom_point(size = 3) +
  geom_line(aes(x=as.numeric(Year))) +
  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
  ylab("Score Differential Varience")

Score differential by team

p <- ggplot(Scores, aes(x=Year, y=Score_differential, color = Home, label2=Score_home, label3=Score_away)) +
  geom_smooth(aes(as.numeric(Year), Score_differential), se=FALSE, method = "loess", formula = y ~ x) +
  scale_color_manual(values = Team_colors, name = "Team") +
  ylab("Absolute Score Differential")
  #scale_x_continuous(breaks = seq(1,17,1), guide = guide_axis(n.dodge=2)) +
  #facet_wrap(~Year)

plot <- ggplotly(p, tooltip = c('Home', 'Score_home', 'Score_away'))
plot